On the ongoing debate about New Zealand Labor Productivity

Everyone agrees that productivity is important for maximizing the welfare of the people of New Zealand. It directly affects wages and investments. In addition, everyone agrees that the way to evaluate New Zealand productivity is to compare it with other countries. However, what measure do we compare?

I plot New Zealand, Japan and the U.S. labor productivity per hour worked (Source: The most recent Conference Board data).

Visually, the plot suggests that the U.S. has been more productive than New Zealand and Japan; New Zealand was more productive than Japan up until the late 1980s; and then almost equally productive from the 1990s to 2016. Not many people would believe that New Zealand has been more productive than Japan.

These measures have trends. We cannot tell what the “average” productivity level is over the past 60 years and it might be pointless to forecast what it will be in the future because the “average” of productivity is a function of time trend, hence misleading.

One way to make the average meaningful is to remove trend from the data. Here too the matter is not straightforward because trend could be different types requiring different treatments, and it is not easy to tell the type of trend. Without going into the technical details, it is widely agreed that the growth rate of such data (i.e., the percentage change from one period to another) is a reasonably simple and a more informative measure than the level.

Table (1) reports the average of the level and the average of the growth rate of labor productivity per hour worked for the G7 countries, New Zealand and Australia over the period 1950-2016. Different conclusions could be drawn from the two statistics. Just like the graph, the average of the level suggests that New Zealand has been more productive than Japan, and Japan is the least productive in the group. The average growth rate, on the other hand, suggests that Japan has the highest productivity growth, New Zealand productivity is the same as Australia, the U.S., Canada and the U.K. but 2 percentage points lower than Japan, and 1 percentage point lower than the other non-English speaking European countries.

 A decade-average of labor productivity growth is in table (2). The high growth of the G7 has been highly influenced by the extraordinary growth of Japan from the 1950s to the end of the 1980s. Average growth of the G7 declined steadily. New Zealand and Australia also experienced high growth in the 1950s and the 1960s followed by a decline. Overall, it does not look very different from Australia and the G7 over the past 40 years. Also, both New Zealand and Australia have higher labor productivity growth than the G7 for the past 7 years.

Table (1)

Average Labor Productivity per Hour Worked (1950-2016)

Country	Level	Growth Rate (%)
Canada	35	2
Italy	35	3
Japan	23	4
France	38	3
Germany	37	3
U.K.	32	2
U.S.	43	2
Australia	34	2
New Zealand	28	2

The Conference Board measures the level of labor productivity per hour worked in 2015

US$ converted to 2015 price level with updated 2011 PPP.

Table (2)

Average Labor Productivity Growth (percent)

	G7	New Zealand	Australia
1950-1959	4	3	3
1960-1969	5	2	3
1970-1979	3	0	2
1980-1989	2	2	1
1990-1999	2	1	2
2000-2009	1	1	1
2010-2016	0	1	1

I conclude that, on average, labor productivity in New Zealand has been the same as in Australia, Canada, the U.S., and the U.K., and lower than France, Germany, Italy, and Japan. 

Housing Price in New Zealand

A long time ago when I was at the RBNZ, I showed that the typical positive correlation between money and prices over the business cycle (deviations of money and CPI from trend) has broken down in New Zealand. I am sure the same is true in other inflation-targeting countries.
http://www.rbnz.govt.nz/research-and-publications/discussion-papers/2001/dp2001-02-2

Successful inflation targeting rendered inflation stationary while money, which is not targeted by the CB, continued to fluctuate, hence no correlation. In economic theory, excess money growth fuels asset price inflation. People buy houses, stocks, etc. Housing is the dominant asset market in New Zealand. Here is a graph of the rate of growth of housing prices and the M2 growth lagged one year. Monthly data are from the RBNZ website (I calculated the quarterly data as monthly averages).

Visually, there is a theory-consistent positive correlation. There is a trend in both growth rates. It seems that this positive correlation between trending housing price inflation and lagged money growth may imply that money affects house price growth in addition to other micro and macro shocks (e.g., building restrictions, land shortages, zoning policies, population growth, etc.).

razzakw@gmail.com 

Why New Zealand’s wages are lower than Australia’s?

The Australians earn more than New Zealanders on average. For example, the Australian Bureau of Statistics reports the average weekly total earning in May 2016 to be 1160.90 Australian dollars while Statistics New Zealand figure for 2016 Q3 was 985.97 NZ dollars. Americans make more too. The microeconomic textbook explanation is straightforward. If (1) we supply more labour than Australians we will have relatively lower wages; (2) if our productivity is relatively lower; our wages would be relatively lower. Both (1) and (2) are evident in the data.

We supply relatively more labour (hours) than the Australians and the Americans. I measured the supply of labour as the average weekly hours worked when I was at the New Zealand Treasury in 2012. Hours depend on the consumption-income ratio, the marginal tax rate, the relative price of leisure, and the share of capital in production. Here is a graph for New Zealand, Australia, and the U.S.

Further, among other reasons for the increase in he supply of hours such as the increase in female labor supply, population of working age, immigration etc. the increase in labour supply (more hours worked) is also consistent with the decline in the reservation wage over time. The reservation wage has been declining relative to the real wage. The reservation wage is the wage equivalent of being unemployed. Most of the theoretical models of wage setting suggest that it depends on past real wages, labour productivity, and the unemployment rate. The reservation wage depends on the generosity of benefits, and other income supports the workers expect to have while they are unemployed. The benefits have been falling over time in New Zealand. The institutional dependence of unemployment benefits on past wage level, may suggest that the reservation wage also depends on past wages. The reservation wage depends also, on what the unemployed do with their time – the utility of leisure, which may include home production and income that, could be, earned in the informal sector. More on this is in Razzak (2015). Here is a plot of my estimate of New Zealand’s ratio of the average hourly reservation wage relative to the real average hourly wage.

And, we are less productive than the Aussies and the Americans. This has been shown in blogs (e.g., Michael Riddell’s blogs), the Conference Board data, and in many other papers to the extent there is a wide agreement.  

razzakw@gmail.com

References:

Razzak, W, (2003), Towards Building a New Consensus About New Zealand’s productivity. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=508262

Razzak, W., 2015, Wage, productivity, and unemployment: microeconomics theory and macroeconomics data, Applied Economics, Vol. 47, Issue 58, 6284-6300

Gender Gap in Math and Science

Most of the publications on the gender gap in math and science are in the fields of psychology and education. The most important questions are about the existence of the gap, what explains it, and whether the gap in early stages of education explains the subsequent career paths of men and women.

Researchers use data of standardized test scores in math and science.[i] The methodology usually compares moments (mean, median, and variance).  Benbow and Stanley (1980, 1983, and 1988), among many other papers, were the early studies to analyzing the gender gaps in mathematical reasoning ability. They presented evidence that male and female students performances in math and science are almost equal, on average, and sometimes females were even better than males, however, females are less likely to choose science and math careers.^[ii] 

Recent studies include The European Commission study (2009), which analyzed TIMSS and PISA data sets to report small differences in average performance, where males’ achievements in math are greater than females at later school years, and are especially noticeable among students who attend the same teaching programs and year groups. It reports very small differences in science in favor of males. And, males are worse than females in reading.^[iii] 

For the United States, Hyde et al. (2008) report that the gender gaps in math in the general population, grade 2 to 11, were trivial. On average, they found no gender gaps in test scores of 7 million students. They also found greater males’ variability, where ethnicity, i.e., whites versus Asian/Pacific Islanders, seems to matter. The causes remained unexplained.^[iv]

Andreescu et al. (2008) examined data from the Study of Mathematically Precocious Youth (SMPY), Putnam Mathematical Competition, and the International Mathematical Olympiad (IMO). They found that culture is not an explanatory variable of the gender gap; and the gender gap in math is smaller in societies with more gender equality in general.

The TIMSS’ report on gender differences (2007) finds a few gender differences in average mathematics achievement at the 4^th and 8^th grades. At the final year of secondary school, TIMSS report that males in 18 out of 21 countries have significantly greater achievements in mathematics. In science, gender gaps in achievement favoring males were present in one-third of the countries even as early as the fourth grade. At the eighth grade in science, the gender gap was even wider with males performing significantly higher than females in nearly two-thirds of the countries.

 

There has been a lot of media interest in this subject because it’s a sensational issue. When Lawrence Summers, Ex-president of Harvard University, said that males are innately better than females in math the news media was on fire. He resigned his position.

There is also a tendency to jump to policy. Many want to spend more money on improving teacher’s ability, increasing teacher’s pay, reduce poverty, build better equipped schools, etc. Everyone has an agenda and search for evidence to support it.

From economic point of view, I do not think we have a problem. Maybe this is why economists were relatively less active in this area of research. It’s unclear how the ratio of male to female mathematicians (the gender gap) or scientists affects the economy.

I tested the gender gap using the TIMSS data. The data are from the study cycles 1995, 1999, 2003 and 2007 for 1,426,402 students drawn from 44090 schools representing tens of countries from all over the world. However, my test is more than testing whether the means (averages) are equal, but rather tests whether every observation in the distribution is equal.[1] Without going into the technical details, the results are summarized as follows. These are two matrices showing the 4^th grade and 8^th grade math and science test scores. They report the percentages of countries in the total sample, where the distribution of the scores of boys dominates; whether the girl’s dominates; and where is gender equality (zero-gap). They also report the same results for the top tail of the distribution (the top 10 percent). I kept the name of the countries in the case of gender equality because they are interesting. Boys dominate.

Gender equality is intriguing and more challenging to explain than the gender gap because equality means no gap, or no variation in the data. We simply cannot explain no-variability using conventional econometric techniques.

The only information available in TIMSS that could be used to shed light on the results of gender equality is about the type of school – the single-sex schools – or the segregated schools. This has been identified in the literature as a cultural issue and that it might explain why boys and girls do equally well in these countries. I repeated the test to test whether school segregation explains the gender equality. The test  cannot reject the null hypothesis that segregated schools distributions is equal to all non-segregated schools distributions, except in a few cases, where females clearly dominate. In math and science, Iran’s segregated schools at the 4^th grade dominate. In science, El Salvador, Singapore and Dubai segregated schools dominate. Segregation explains only two of our original results for gender equality and females’ dominance (Singapore and Iran. In the 8^th grade case, the test provides a little more support and explain why some countries have females’ distributions dominate, but the results do not explain that for every case where there are no gender differences or females’ domination. One wonders about the cultural common denominator between Iran, El Salvador and Singapore. I see none. Further, many of the countries, which have gender equality in math and science, do not have gender equality in general.  

Jonathan Kane, one of the authors of the paper cited above, has been reported saying that “girls living in some Middle Eastern countries, such as Bahrain and Oman, had in fact, not scored very well, but their boys had scored even worse, a result found to be unrelated to either Muslim culture or schooling in single-gender classrooms.”   

New Zealand is one of the OECD countries in addition to Canada and Sweden among all other non-OECD, Russia, and Islamic and Arabic countries, where there is gender equality. So we have no gender gap issue in New Zealand to waste our time with. Boys and girls are equally good or bad in math and science. I do not know whether this is a bad or a good outcome, but surely it could be good news for those who care about equality. However, if Kane is right then there should no reason to celebrate the gender equality result I found.    

Summary of the results

Matrix I- The 4^th Grade, TIMSS, 1995, 2003, and 2007

	Math		Science
	Full sample	Top 10%	Full sample	Top 10%
Males Dominate	56%	66%	53%	74%
Females Dominate	12%	11%	14%	2%
Equal	1. Greece; 2. Ireland; 3. New Zealand; 4.Algeria; 5. Kuwait; 6. Qatar; 7.Yemen; 8. Dubai; 9. Iran; 10. Georgia; 11. Latvia; 12. Russia; 13. Ukraine; 14. Chinese Taipei; 15. Singapore; 16. Mongolia 32%)	1. Greece; 2. Ireland; 3. Algeria; 4. Tunisia; 5. Moldova; 6. Georgia; 7. Russia; 8. Ukraine; 9. Kazakhstan; 10. Philippines; 11.Thailand; 12. Mongolia (22%)	Canada (BC); 2. Israel; 3. New Zealand; 4. Sweden; 5. U.K.; 6. Algeria; 7. Kuwait 8. Morocco; 9. Qatar; 10. Yemen; 11. Dubai; 12. Iran; 13. Latvia; 14.Lithuania; 15. Russia; 16. Ukraine; 17.Kazakhstan (33%)	1. Norway; 2. Sweden; 3. Algeria4. Morocco; 5.Tunisia; 6. Dubai; 7. Moldova; 8. Georgia; 9. Russia; 10. Kazakhstan; 11.Philippines; 12.Mongolia (24%)

Matrix II - The 8^th Grade, TIMSS, 1995, 1999, 2003, and 2007

	Math		Science
	Full sample	Top 10 %	Full sample	Top 10%
Males Dominate	58%	78%	70%	83.33%
Females Dominate	7%	1.37%	7.1%	5%
Equal	1.Finland; 2.Iceland; 3.New Zealand; 4.Norway; 5.Slovenia; 6.Sweden; 7.Egypt; 8.Kuwait; 9.Jordan; 10.Oman; 11.Palestine; 12. Qatar; 13.Saudi Arabia; 14.Syria; 15.Dubai; 16.Bosnia & Herzegovina; 17.Iran; 18.Moldova; 19.Malaysia; 20.Georgia; 21.Macedonia FYR; 22.Lithuania; 23.Romania; 24.Russia; 25.Ukraine; 26.Chinese Taipei (37%)	1. Canada (BC); 2. Finland; 3. Iceland; 4. Syria; 5. Dubai; 6.Bosnia & Herzegovina; 7.Iran; 8. Moldova; 9.Malta; 10.Cyprus; 11.Armenia; 12.Georgia; 13.Ukraine; 14.Philippines; 15Mongolia  (20.54%)	1. Algeria; 2.Egypt; 3.Kuwait; 4.Jordan; 5.Oman; 6. Palestine; 7.Qatar; 8.Saudi Arabia; 9.Bosnia & Herzegovina; 10.Iran; 11.Moldova; 12.Malta; 13. Macedonia, FYR; 14. Serbia; 15.Philippines; 16.Mongolia (22.85%)	1. Canada (BC); 2.Lebanon; 3.Dubai; 4. Iran; 5.Moldova; 6.Malta; 7.Armenia. (11.66%)

Referencs

Andreescu T., A., J. A. Gallian, M. Kane, and J. E. Mertz, (2008), Cross-Cultural Analysis of Students With Exceptional Talant in Mathematical Problem Solving, otices of the AMS, Vol. 55, no.10, 1248-1260

Benbow, C.P., (1988), Sex Differences in Mathematical Reasoning Ability Among The Intellectually Talanted, Behavioral and Brain Sciences, 11, 169-183, 225-232.

Benbow, C.P. and J. C. Stanley, (1983), Sex Differences in Mathematical Reasoning Ability: More Facts, Science, 2222, 1029-1031.

Benbow, C. P. and J. C. Stanley, (1980), Sex Differences in Mathematical Reasoning Ability: Facts or Artifacts? Science, 201, 1262-1264.

Benbow, C.P., D. Lubinski, D.L. Shea, and H. Eftekhari-Sanjani, (2000), Sex Differences in Mathematical Reasoning Ability: Their Status 20 Years Later, Psychological Science, 11, 474-480.

Hutchison, D. and I. Schagen, (2007), Comparisons between PISA and

TIMSS: Are we the man with two watches? In Loveless, T. (Ed.), Lessons learned: What international assessments tell us about math achievement, 227–261, Washington, DC: Brookings.

Hyde, J.S. and J. E. Mertz, (2009), Gender, Culture and Mathematics Performance, Proceedings of the National Academy of Science, USA, 106, 8801-8807.

Hyde, J.S., S.M. Lindberg, M.C. Linn, A. Ellis, and C. Williams, (2008), Gender Similarities Characterize Math Performance, Science 321, 494-495.

Hyde, J.S., (2005), The Gender Similarities Hypothesis, American Psychologist, 60, 581-592.

Hyde, J.S., E. Fennema, M. Ryan, L.A. Frost, and C. Hopp, (1990), Gender Comparisons of Mathematics Attitudes and Effects, Psychology of Women Quarterly, 14, 299-324

  

      

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[1] First-Order Stochastic Dominance is tested using the powerful Rank Sum Test of (Wilcoxon, 1945).

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[i] Commonly used data include Program of International Student Assessment (PISA) of the World Bank, Trend in International Math and Science Study (TIMSS), The U.S. SAT test scores, Study of Mathematically Precocious Youth (SMPY), Putnam Mathematical Competition, and the International Mathematical Olympiad (IMO). 

[ii] Also see Benbow and Stanley (1983), Benbow (1988), Benbow (1992), Benbow et al. (2000)

[iii] Also see for example Hutchison  and  Schagen (2007).

[iv] Also see, Hyde (2005), Hyde et al. (1990), and Hyde and Mertz (2009). 

Trans Pacific Partnership TPP and New Zealand

I have not written about New Zealand since last year because I did not find anything important to write about. However, things changed a few days ago. There is some press talk about The Trans Pacific Partnership TPP. It is not published yet. We do not know what's in it. And even if it is published I am sure it would be a boring document written by lawyers. Economists cannot possibly digest all of it. Despite that, some people wrote about what they now about it (leaks perhaps) in the press around the world. What has been written could be true or not, we do not know for sure. The whole thing has been secretive and argued about behind closed doors and definitely inconsistent with democracy.

Here is my quibble with it. It has been reported in the press that dairy products are not included in the partnership. So countries that have been subsidizing dairy products will continue to do so. New Zealand is a dairy exporter, which does not subsidize dairy producers. The question then, what benefits New Zealanders expect from this partnership? Did the New Zealand government calculate the expected gains from TPP? The way I see it is that our dairy producers could not possibly compete more than they do now in Japan or the US or any other country where their dairy products are subsidized, could they? 

A second point is that New Zealand is not an industrial country. We do not have a manufacturing sector that could possibly compete with Japan or the US. So how could we benefit from free trade in manufacturing? Maybe we get cheaper goods and consumers be happy, but there would be no obvious increase in GDP from this type of trade, would it?

I am for free trade, no doubt. But I am sorry I do not see the benefits from this Partnership. I hope that I am wrong and that TPP is worthwhile otherwise New Zealanders have lost a lot already. 

razzakw@gmail.com 

Big Data and Economic Policy

A useful book to read on Big Data is entitled “Big Data,” by Viktor Mayer-Schonbeger and Kenneth Cukier.

No rigorous definition of Big Data is available. According to the book, essentially, Big Data means “things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, and relationship between citizens and governments, and more.” The question is how this new phenomenon can affect economic policy, such as monetary policy.

The book discusses a few important issues surrounding Big Data, which are worth mentioning here, and may require pondering. First, the sheer number of observations, or N=all, implies that the principle of randomness, which we use in statistics, is no longer applicable. Second, Big Data maybe messy, hard to structure and tabulate, but the authors argue that “more trumps better”. Indeed, MIT economists Alberto Cavallo and Roberto Rigobon collected half a million prices of products sold in the US every day, and used them to measure inflation. While it is a messy data set, they claim that they have immediately detected the deflationary episodes in prices after the Lehman Brothers collapse in September 2008, while the official CPI data showed that in November 2008. Third, Big Data could tell us more about correlation between X and Y, for example, but nothing about causality. Fourth, correlation is used in predictions. Fifth, Big Data handles nonlinearity better than small samples.

I do not have much quarrel with these assertions.

Let us talk about policy. Take for example monetary policy in the US, the EU, New Zealand, and in a number of the advanced countries, where the primary objective is price stability. Regardless of the operational details of the policy, which vary from one central bank to another, the policy is, essentially, demand management. Demand management is based on economic theory: as data arrive in time, central banks try to discern the nature and the permanency of the shocks. When the shocks are thought to alter the future paths of output and prices, central banks intervene by moving the current short-term interest or change the current money supply, its growth rate,  or whatever the policy instrument is. This forwardness is the essence of monetary policy because we know that monetary policy affects the real economy with a lag, and that these lags are long and variable. For example, for the central bank to change the future path of output, it moves the interest rate a year or a year and a half earlier. A popular economic theory among central banks predicts that when central banks project output to be above its potential, i.e., a positive output gap, aggregate demand increases, and inflation would increase above its expected level.

The problem is that the most important variables within the demand management framework are unobservable to the policymaker. The central bank needs to estimate, propose, or calibrate these variables. Neither potential output nor expected inflation is observable. Similarly, the Wicksellian Natural Rate of Interest, which is also of interest to some policymakers, is also unobservable.

Given that most of the policy-relevant variables are unobservable, the question is how could Big Data benefit policymakers? I suspect that “more data” can actually help policymakers measure the values of the important unobservable variables needed for policy-making. However, Big Data may provide an estimate of future aggregate demand for goods and services that the policymaker can use to infer future inflation. Alternatively, Big Data could tell us whether we would have higher future inflation directly without inferring it from excess demand, i.e., as in Cavallo and Rigobon. That would raise a number of questions. For example, would that mean the end of economic theory in the conduct of monetary policy? If super computers can tell the policymakers that prices of goods and services are going up with such accuracy, could the policy be tightened, i.e., increase the short-term interest rate? If so, there would be no more fuss about models, estimations, predictions, etc. Could this be the future of monetary policy? The models used by central banks are based on a set of assumptions, which reflect a certain economic paradigm or belief. Therefore, it is hard to change how central banks think and work. Milton Friedman advocated a different way to do monetary policy, i.e., the x% money growth rule, but it was highly resisted by central banks because implementing such policy regime would have left very little for them to do. 

Even if Big Data could tell us something about inflation a couple of months ahead, such as in the Cavallo and Rigobon’s study, it would not be sufficient. They say that they knew in September 2008 that prices were falling, while the CPI data showed that decline in November 2008. That might be true for the public, but it cannot be the case for economists working in the central banks because they actually would know the prices of more than 70 percent of the goods and services in the CPI basket before November. Indeed, central bank forecasts for the next quarter’s CPI is accurate most of the time. Instead, the one-year-ahead inflation rate is most relevant for policy, which is very difficult to forecast. Could Big Data tell us what would inflation be a year ahead?

One could imagine scenarios where Big Data could shed more light on aggregate demand. For example, one could find out whether millions of people, for example, are shopping online for a new car, new homes, or durable goods in general. That might be a useful signal about future aggregate demand. Would policymakers alter policy because of such information? Similar information could be obtained online about imports and exports, which affects aggregate demand. This idea is not very different from measuring vacancies by counting job ads on the Internet, which has been used to fit the Beveridge curve (the empirical relationship between vacancy and unemployment).

Government’s security agencies seem to be benefiting from the Internet Big Data phenomenon, but other departments have not invested yet in it. Also see an article by Kalev Leetaru published in Foreign Policy in May 29, 2014. He uses Global Database of Events, Language, and Tone Online Library for 2.4 million protests to analyze the Arab Spring. I think that the time will come. In the past, central banks used large-scale models, which failed to increase the forecasting accuracy and to make a better policy. Then central banks used factor VAR’s, where a large number of variables are used. The forecasting accuracy did not increase either. I do not think Big Data would improve forecasting accuracy in economics, but that does not mean that central banks will not explore this avenue. I have a feeling that various governmental departments, and the central banks, are likely to be investing in Big Data this decade. 

Razzakw@gmail.com